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Introduction
A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
Your role and responsibilities
As a Data Scientist specializing in Artificial Intelligence, you will help design, build, and deploy AI and machine learning solutions that solve real business problems. As an entry-level team member, you will work under the guidance of senior data scientists and AI architects across the full project lifecycle.
Take part in end-to-end AI and ML projects, from problem definition and data collection through model deployment and monitoring.
Work with business stakeholders and subject matter experts to understand requirements and translate them into analytical problem statements.
Collect, clean, explore, and preprocess structured and unstructured data from multiple sources.
Perform exploratory data analysis (EDA) to uncover patterns, trends, anomalies, and relationships in data.
Build, train, evaluate, and fine-tune machine learning and deep learning models for tasks such as classification, regression, clustering, natural language processing (NLP), and computer vision.
Apply statistical analysis, hypothesis testing, and feature engineering to improve model performance.
Use frameworks such as TensorFlow, PyTorch, or scikit-learn to develop and experiment with models.
Help integrate and deploy models into production environments using MLOps tools and cloud platforms.
Build dashboards and data visualizations to communicate findings and model results to technical and non-technical audiences.
Document methodologies, experiments, model performance, and assumptions in a clear and reproducible way.
Follow responsible AI principles, including fairness, transparency, explainability, and data privacy.
Stay current with the latest AI research, tools, and techniques, and apply relevant advances to ongoing work.
Work with cross-functional teams including engineers, architects, and business analysts, and report progress to the project lead.
Required education
Bachelor's Degree
Required technical and professional expertise
Strong foundation in mathematics, statistics, and probability, including linear algebra, calculus, hypothesis testing, and distributions.
Proficiency in Python for data analysis and machine learning, including libraries such as NumPy, pandas, and Matplotlib.
Working knowledge of machine learning algorithms, such as linear and logistic regression, decision trees, random forests, support vector machines, and k-means clustering.
Understanding of model evaluation metrics, cross-validation, overfitting, and bias-variance trade-off.
Basic knowledge of deep learning concepts, including neural networks, activation functions, backpropagation, and common architectures (CNN, RNN, Transformer).
Familiarity with at least one ML framework such as scikit-learn, TensorFlow, or PyTorch.
Working knowledge of SQL and experience querying relational databases.
Understanding of data wrangling, feature engineering, and preprocessing techniques.
Good verbal and written communication skills in English, with the ability to explain complex findings simply.
Strong problem-solving, critical thinking, and analytical skills.
Willingness to work in a team, learn quickly, and take feedback.
Preferred technical and professional experience
Hands-on project experience in machine learning, NLP, computer vision, or generative AI through academic work, internships, or personal projects.
Familiarity with large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), or fine-tuning techniques.
Exposure to MLOps practices and tools such as MLflow, Kubeflow, or DVC for experiment tracking, model versioning, and deployment.
Experience with cloud AI platforms such as IBM watsonx, AWS SageMaker, Google Vertex AI, or Azure Machine Learning.
Knowledge of big data tools such as Apache Spark, Hadoop, or Databricks.
Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
Experience with version control using Git and collaborative development workflows.
Knowledge of data visualization tools such as Power BI, Tableau, or Plotly Dash.
Understanding of Agile or Scrum delivery methodologies.
Published research, Kaggle competition participation, or open-source contributions related to AI or data science.
IBM AI Engineering Professional Certificate, Google Professional ML Engineer, AWS Certified Machine Learning, or equivalent credential.